{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "559b3cf6",
   "metadata": {},
   "source": [
    "# Markdown\n",
    "## Text"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8d36d22",
   "metadata": {},
   "source": [
    "- fix point interation\n",
    "- bisection\n",
    "- Newtown iteration\n",
    "- secant"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "404e6a4f",
   "metadata": {},
   "source": [
    "## Math equation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14ac4d68",
   "metadata": {},
   "source": [
    "### simple math"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac3d51cc",
   "metadata": {},
   "source": [
    "$1+1=2$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "623d3504",
   "metadata": {},
   "source": [
    "### greek letters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e01f3b83",
   "metadata": {},
   "source": [
    "$\\alpha + \\beta = 1$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e737114",
   "metadata": {},
   "source": [
    "### fractional\n",
    "$\\frac {1}{3} + \\frac {2}{3} = 1$\n",
    "\n",
    "$\\frac {\\sigma}{3} + \\frac {\\gamma}{3} = 1$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4de5cf21",
   "metadata": {},
   "source": [
    "### root\n",
    "$\\sqrt 2$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59de4027",
   "metadata": {},
   "source": [
    "### exponential\n",
    "$\\alpha ^ a$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eca5d30",
   "metadata": {},
   "source": [
    "### vector\n",
    "$\\vec a$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2292b7f9",
   "metadata": {},
   "source": [
    "## image\n",
    "![image](https://markdown.com.cn/assets/img/philly-magic-garden.9c0b4415.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f915aac5",
   "metadata": {},
   "source": [
    "![image](2021.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b743dbc1",
   "metadata": {},
   "source": [
    " <img src=\"2021.jpg\" width = \"300\" height = \"200\" alt=\"图片名称\" align=center />"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "816a75c5",
   "metadata": {},
   "source": [
    "## Numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0133de6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "mylist = [1,2,3,4]\n",
    "mylist2 = [2,3,4,5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5f87203b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 2, 3, 4, 5]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mylist + mylist2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "538f55c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 1, 2, 3, 4]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mylist * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c3b3bd2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "44b81a77",
   "metadata": {},
   "outputs": [],
   "source": [
    "myArry = np.array(mylist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dc3f109e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myArry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3c756339",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 6, 8])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myArry * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "17c8bf8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  4,  9, 16])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myArry * myArry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ac618730",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  8, 27, 64], dtype=int32)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myArry ** 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0d01aeec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0.],\n",
       "       [0., 0., 0.]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((2,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "64ef1891",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((4,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "60b356de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0., 0.],\n",
       "       [0., 1., 0., 0., 0.],\n",
       "       [0., 0., 1., 0., 0.],\n",
       "       [0., 0., 0., 1., 0.],\n",
       "       [0., 0., 0., 0., 1.]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.identity(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e8f501e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "myIdentity = np.identity(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cf4f60a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 0., 1.]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myIdentity * myIdentity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ad6f9ea7",
   "metadata": {},
   "outputs": [],
   "source": [
    "myOnes = np.ones((3,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e7e42d43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myIdentity @ myOnes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f9d54b1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "array1 = np.array([[1,2],\n",
    "                  [3,4]])\n",
    "array2 = np.array([\n",
    "    [1,2,3],\n",
    "    [4,5,6]\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7bd535ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 9, 12, 15],\n",
       "       [19, 26, 33]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array1 @ array2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "76c9c01d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c5812731",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(-10,10,0.1)\n",
    "y = np.sin(x) + np.sin(x * 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4465d2d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x20ceb0101f0>]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ff7tYRVyncBonHvKhVGsMVU3wXqBp4auCs2zBwm/58A0tfFlPBwDWcnubvfM+DlZWWCmBKDVg+JLfpOAfALg1knDSwjdI+uEMm4XTCwqVOorVxh4fPt9AXLG4wgp63Qj6tAuncWKyYikApdl7Z6OegNeNibDPUrazsA9fFfxhidSRgn8A4I2sO106M2MBuF1kycLX6+XZDrcwRzn5ivf17bTwA143EmGfJQt/u1AzjJACdgRnlCfcfKWOQrWxx6UDKJ8BSxZ+sQqPizAW0K+nz334w5LtLAX/AMAbWSejuwXa43ZhZixgycLfLlSFLHxZE3+nFWSn4APKxq1VH77IhBuTK6yWoHe6dABllbVicdN8PNy9UimHFxccluQ3p1ocfoGI1onoUpf3iYg+TUTXiOgCET3kxHUlCrwet1Z3pfm4tVj8dFHMwtxxKQyHhdMLdpq97xWc2VjA0qahSJYnICuWAsC66rLR6r08EwtYDss08t8DaK0AhiU02SkL//cBPKrz/mMA7la/ngTwWYeuK4ESkqksP/cK9Fw8YDrbk2cZighOrOXDrJu6xkHCyMK3knyVKoqtsPgeyrC4FHoB99FrWfizY9ZyIZTmP8YGj8ftQtTvGZoVliOCzxj7JgC94utPAPhDpvACgDgRzTpxbQmwla8gEfHBpRFRk4z6sZmvmIrTbhVOExD8MelDxnquAq+bWv70dmZjAeQqdWRNZnumBC38gNcN/5DVZHeaVlkFDcHntXXWs+Zi8UUNHkD5DAzL+PfLhz8P4E7bz4vqa3sgoieJ6BwRndvY2OjLzQ07m/m9MficZNSPcq2JfEXcAk8JllUAgIjPAxfBtKAdJDZyFUxG/JoT7qwaGmvGyq81msiW60IWPsCT30Z3/NeyZUT8HoQ1GrnzbFuzkTopwT0sYLgqZvZL8LV2PjRNTsbYU4yxs4yxs8lksse3dTDYzFd0BR/YcTuIwGvjiDzwLhchGhieB74XKElX2uM/bWH8063m5cYTLsArNo6uS2e7UG21G+yEC76ZfZRmkyFVrCIhaOEPU8XMfgn+IoBDbT8vAFju07UPPJu5StcHPhlRHnhTgi+Y5cmJBb1DE4fcC7SSrjitCTcvbmHy8ddrL9nOqNfE12vFOd0qryA+/plSDU1mHIPPkRb+Xp4B8FE1WucRABnG2Eqfrn2gYYxhM19F0sjCN9FoWQqOOTZyZWPBNzHhtnoRmJpwR3fTPFOqae6fAEA04EXY5zbl0tm2YPAMi0tNP6tAECL6EwDvAzBJRIsAfh2AFwAYY58D8CyAxwFcA1AE8I+cuK4EyFXqqDaajrp0zArOWNCDbHk0BYfXMeo2VhG/BwGvy6RLTaysAmcs6MUbK9YbfQw7qWIVx9r6EHQyNRbo6fjzmviMMcO4/f3GEcFnjH3E4H0G4BNOXEuym1aWbVT74YwHvfC4yKRLpwainZBLI2JBL9ay1ptFDzP5al1Z/ncRByJCMuo3N+GatDDHAqPtUksXa7qr0WTE3PhvWVhhVRtNlGoNhHyOSGrPkJm2Q45e0hWgbKpOmnzgU4UqYgKF0zhjI7xpy8Px9CbHZMTf+juJwN0DcYEoKX7tXKWORnP0SiTXG03kynXdsUpG/eZcmhYEHxiOXBQp+EPOdkF5kPUeTh6LL3zOoliWIWeUN213xLn7eJmdcLcLVYR97l3ds/TggmOls9Owww2Nbj58AJZXWOIuneHJRZGCP+Rwq0LXwjRp4aQF67hwxoJeVOrNkSyRzMMhnRx/0cJ1nFFOfksJ9F5ORv3IlevCz6dopVLOWFBx4wzD+EvBH3J4wpNeo5LJiM+khSlWR4fDrz2KyVci7pdk1I/tQlW4Dd62YFkFzjC5FJwmUzKOKOMRbKKfgS3BLGeOtPAlfSNbUjZYIzqbRYpLp4qmoI/XTJYh0C44g//AO01a0KUAAFuCfnyzFv4wCY7T8CRBkfEXdWuKlrXgDNP4S8EfcrLlOsYCXs20fk4y4kdDzR40gjHlODMPPK8YmBlFC1MdU70VllkLU9lDMbPCGh6XgtPwCVfPQDEbmrxdrFmacIfB4JGCP+RkSrXWB74bSbVsr4gfuVRroFJvCiddAcP1wDtNplRD0Ku/wWo22zZdsCg4I+lSU/dQDFxqgHjyYapgbsKNBqSFL+kT2VJNsyxyO2YsnJ2kK/M+/GF44J1GiQEXG//NnPEKq1pvIlepm46SAkZ3/N0Gnakmwj4QiVv4Zl1qbhchGhiOEslS8IecbNlZweebkJZ8+KNoYZZqhglqPEdCxMJslbUwIThBrxseFw2F4DhNqqjkjOhluHrdLkyExAIXKvUGcpW6cOE0zrAkv0nBH3KypbqAS8e8hW8qLJAvaYeknoiTZIrGgh/wuhENeITGn2/smhEcIhrZXIh0yXiFBYjH4qcFwjy1GJZ6UlLwhxwRCz/scyPodQs98CmTSScA4PO4EPS6R9TCrzoqOGbrGHGGRXCcJl2s6kbocERzIVrjb+L5B4Zn/KXgq2SKNfyTP3oZt7cK+30rpsiWaroRIkBbPRcTD7yZOHzAmQf+N59/E3/28qKtc/SbdLHW6iurh2g9ly01c9qsSyHqwPjf3Czg/3nxLVvn6DdK72Xnxj9lYYULqNnmDhg81bpYroZVpOCrPP29RXz10io+/fVr+30rwtQbTRSqDUMLHxAvr7BdqMJF4qWROWNBj63En9eWM/j016/it55/01Q7xv3GlEvBjIVpSXCsj/92oYqf/r0X8StPX8Riqmj5PP0mXazpRuhw+ArL6NnihdPMTrh2DZ7Xl7P4h59/ESf/1VfxtdfXLJ/HCCn4Kl8+r/Rj+fKrS6a64+wnuTIvq2BcoU/UwtnMKzH4ooXTOHYf+N/578pEu5Qu4eJSxvJ5+km5pvT+NSM4RmwXqiALE64dHz5jDP/0T17Bitok5DvXtyydZz9IC2YlJ6N+VNQIKD1aLk2zgm+zCcq//8vLuLiUgdftwt9e27R8HiOk4AO4vVXA995K4x8+chcYgN/71s39viUh+ANm5NIBzAhOxbR1CdirmHl1LYevXlrFTz9yGB4X4dmLq5bO029aZRVEXDpRP/KVOkpV/Xou22qWs9kJd8xGWOB6roJvX9vCP/vA3UiEffjOjeEQ/GpdWeGKuB9FAxf4CktkX6CdsYAH5VoTlbr5elKMMVxcTOPR+2fwtoU4Xr2TNn0OUaTgA3jmVcW6/yfvO4EfPDWFv3xtOASnVUdHwKUzGfEjVawZ+gi38lUkwtqllvWw48P8mzeVZvX/9O+fwPefmMRXL60MhVtHpHAah2fbGrnV9Jqp6MEtfCvjdnlVaZ7yjsMTeORYAt+5vjVc4y/owweMBZ+XBve4zUmjnVyIxVQJqWINDyzE8OBCDK+vZHvmy3dE8InoUSK6QkTXiOhTGu+/j4gyRPSq+vVrTlzXKb51dRNnDsUxHw/i1EwUy+lSzzdPnID7zEUtfGBnU7Ab24UqJrr0x9VjzIZL5/pGHuMhL6bGAnj0/hnc3iri+sbgN1QxU7d+Uh3/dQPBMVu4ixMLelFvMhQNVhBaXFnNAgBOzUTxyPEEVjJl3N4afD8+d2EJTbiiFn6xZtp/D7QVELTwGbikujBPz8dw5lAc1XoTb671poOZbcEnIjeAzwB4DMB9AD5CRPdpHPotxtjb1K/fsHtdJ7mxWcDJ6QgA4K5EGE2Godi42qmUKeDDF3zgN/MVTFp84POVunCBtnaurxdwYkoZ//vnxgAANzcHf/zTAs1POKIW5nahaktwrEy6l1dzmIr6MR724fuPJwBgKNw6vHaTXpYtR9ylUzHtvwfaLXzzG+cXlzLwuAinZqI4sxAHgJ65dZyw8B8GcI0xdoMxVgXwRQBPOHDevpAr17CZr+CI2hPzcCIEALi9PfiCw60J0SgdQP+Br9abyJbrSHTpnqVHLOgFYzsbyWa4vpHH8aQi+PPjQQDDMeHulOY1Hv8pwXoudlw6gLVs5yurOZyciQIAjk2GEQt6W1bnIMMbvkQFnv9Y0AuvmwTGXyzMU+v8gDUL/+JSBvdMRxHwunFoIojxkBcXFtOmzyOCE4I/D+BO28+L6mudvIuIzhPRV4no/m4nI6IniegcEZ3b2Nhw4Pb04UvXowlV8CcUwX9rGJa0ArXwOSKCnzLZS7WdnYqZ5h74VKGKrUK1ZeEnwj4EvC4spQY/UsqMhS9Sz4VXNLVi4bcsTJPZzvVGE1fX8zilCj4RYT4ebEXsDDJZE1FqRCQUqaaURja3YQtYX2ExxnBpKYPT87HWfT64EMeFxd5MuE4IvlY4Qee6/hUAhxljZwD8NoA/73YyxthTjLGzjLGzyWTSgdvT5+amkmjFLfxk1I+g1z0kPsw63C5CWKAzz6Tql9d74PmG4qQFH75VC5P76rmFzwVncQgEP1tWCndF/MaC43G7kAjr13NJF6tgzOqEa01wbm0VUa03cXJmrPXabCyA5fQQjL+JFS5gHKnGGFOaz9hy6Zgb/+VMubVhy3nvPUkcn4pYco8a4YTgLwI41PbzAoDl9gMYY1nGWF79/lkAXiKadODatrnFBV+18IkIhxMhvLU9+Bm3SlkFj27hKI7f40Ys6NVd0vKQNCsuHasWzrV1RfC5hQ8AC+MhLA2F4NQRFRx/wLi3bSvpyqJLDYDp5KsraoQOt/ABYDYewGp28C187j4UcekAxoJfqCp5FbZWWCaff57Zf1w1OAHgZ95zFJ/5nx/S7XFhFScE/yUAdxPRUSLyAfgwgGfaDyCiGVI/FUT0sHrdgdgVurlVwMxYYFf/yrsmQkNh4WcEyiq0Y5Rtywt32fIhm3zgr2/k4fe4MBcPtl5bGA8OhQ8/V64hKrBhyDHKtrWa5QlYF5wrazm4aPeEOxsLIl2sGeYM7DfZcg1eNyHgFZMxo/FvlVWw4MP3ul0I+dymx39NnVhn257/XmJb8BljdQCfBPAcgDcA/Clj7DUi+jgRfVw97CcAXCKi8wA+DeDDbEACfW9tFnBkMrTrNcXCL/ZkSeUkIrXw2zHyYbZcOhbi8O1Y+MeSkV2JRvPjQaSKNRQMsiL3G95tTJRk1I9NEQvfguBHLO6hLG4XMTMW2NXAZS6uNMxZHvCMc2XC1S+N3E4y4sdWvoJGl8+1nfEHrGU7872SmbGApWuaxZE4fMbYs4yxexhjxxlj/0597XOMsc+p3/8OY+x+xtgZxtgjjLG/c+K6TnBrq4ijbcspQAnNrNSbWMsN9rI2WzYujdxOMurXjQPfLlThcZGpc3Ks+/ALOJ7cPf4L48oEPOhunZxApdJ2khHFwuxm69ix8HkTDrOCs5wp7VpdAcDMmPLzSnrAn/9SXSgkk5OM+tFkO8LeCTeGrLg0AWvlRVYzZcSC3l0ehl4y0pm2mVIN24Vqy3/P4ZE6g+7WMWvhT4/5sZYtdxcctY6OqMXUTtjnhttkE45mk2ElU8Khid0rrPn4cIRmch++KMmovxX6qsV23lodF45VC7PTnTAsFn62bN6lCXQPXOAGnlVr20p5kZVMGbOx/lj3wIgL/q2OCB3OXaoADXqkiEgt/HZmYkGUa82uD+VWoWrZuiEi0/VctgpV1BoMcx0P/CE1Fn/QQzOdFpztQgVjAQ+8JtP6OWYFhzGGlUx5z/jPqD+vDnhoZq5sfsIFuudCrGUrILIWpQZYyzZfy5Yx3Sd3DjDigs9DzxbGd1s4rTIEgk2P9wuRblftcEuiW4z1VqFiyZ3AUSxMcb87F5SZ2O7xn4z44fO4Bn7CNS04EV5eQXv813MVTNn48JutZ7RVqKJab+6xMP0eNyYjvoGvGmt+D0v5f3abcNezZUxG/Kbr6HAsr7Ckhd8f+A555xIu5HMj4HW1fKqDSLXeRKkmVgufM9MSfO0P8la+ioRF6wYwb+Hw++h84F2uwY/FbzQZ8hVzm7bcddLNN273w2/Wh8zvQytCZCYWwPKg+/BNRklNRvVzURRr29oKFzDfk6DWaGIzX2l9LvvBSAv+arYCr5v2hGERERJhsYYh+wVPKxepxc7Rs/AZY9jKWyuNzDFrYfJYb60HfnrM39USHgTyZfHCdRw+/t2Smuwu78eC5lxqfFN8LrZX8GdjwYG38HMmo6RCPg8i/u69hdeyFUxH7U24+Uod9YZY4cX1XAWM7TV4eslIC/56toypaEAzwSER8bXi0geRjMksQ0BxKbhI2zebLddRqDY0P/yimLfwy/C5XZr9QxNh/0CvsLKtOi7iFmbA60Yi7MOyxvjXG02s5yq2LXwzLrXWCiu+95pzscBAl1eoNZooVhumJlwAmBrzYzWrPZGt58q2XGrcvSpq5a+q4y99+H1iVWcJlwj7DEsJ7yfZloUpLjgetwtTUe0PMrc6O0P0zDAWMO/Dn475NSfcibCva/jcIGCmF0E7c/GgpoW/ma+i0WS2lvexoBcltQuXCCuZMnwel+a+zUwsiFy5jvyA5kLkW1m25kKIF8ZDmsEAinulatulA4jnQvDP4awNI8ssIy34a9ly1w9YIuIfaAvfbB0Rzmw8oGnh7wi+XQtTvAnHcrqE2THth30i7EO6WBNeHvebrInSvO3MxbXr1HTbzzCD2eS35XQJc7GAZhjuoAcuWJ1wu+0NcTePHWvbrODvBC1IC78vrGcrmOris+MunQFJCN6DmUqZ7czGAprx1dzNMG/Hwg96UG00Ua6JifSqzoTL9xLSNvqE9hKr488t/M7naieAwPr4m01+UzaJu024yrkGdZXFJ1zzFn4QW4UqitXdKxc+/v208FczZQS9btNGgx1GVvALlTpylXrXGX0y7Ee10RzYJe2OhWlOcGbGgljN7E2+Wk6X4HUTJi3G4QPmBIfHgHezaLngD6rg8MJdpl06sSAK1cYe19eKA9aeWQt/JV3S9N8DO/VkeMnsQSNnccLlIdidq6y1rGLhdzMARTBbT2olqzz/VhIdrTKygt+yqGJdfPhqeOKgunXMdLtqZzYWQLHa2LOxtJwuYTYWtFWhz0yJXt5ft6tLLTzg418yv2kL7OyRdK6yVlV/ukhD7m6YGf96o4m1XKXrJj2fcFOFAV9hmZxwueDfSXUKPrfw++vS6ac7Bxhhwechgd3CsHjG6aBu3GZLNXhchKDXXA2OblmUy+mSLf89YM7CMfJZ8766g27hmxd87dBMvtqxY+2ZGX+jTWJe3mFQLXzrLh3tLPq1bBluF9lKPDS7wpKC30fW1SXctIGFuTmoFma5hlhQvFIgZ7ZL8tVyumwrQgcwZ+F0y7Ll8FDN7UEVnHINIZ/bdFYm3yPpFPzVTNl2xUQzgm9k0Ub9HnhcNLATrtU9lGTED597b0e1tWwFU1HtiDFRAl43fB6X0Pg3m0wJGuljSCYwwoK/avDAD7pLJ1Oqm37YgR0Lvz2Lst5oYjVbtrVhC5izcHZC0vQtzO0BHX+zaf2cyYgfXjdhqSOLdTVrP8Weu/dExn+9FZWi7dIkIsRDvsG18NUVlki3sXZcLsJcPLCnMJ/dGHyOaLbzZqGCepP1NekKGGHBX8uWEfF7uj4wEy0f8uC6dKzs7s/GgvB7XK3WgoDy4W80mW0Lf6K10ScgONkyXISum8RetwtjAQ+2B9SlljNZmprjcpFatmDHwmSMqTkJ9j78fo9SEkQk8Ydb+HqblBNh78Ba+LlyDVG/Z1cfBVHmx/eGZt7cLLSK9tlBVPCNVri9YqQFf0onBMvvcSMa8Die7dloMrx8e9t2uKfZSo0ct4twciaKy6vZ1mtcfOxaG9GABy7a6Rykx1q2gsmIX/cDm4gMbrZtVm2+YYW52O6OXtuFKqqNJmadsjAFJ1yjypDjIZ/jm7bffHPDkX65WYsrXABYiO9uoZkr17CYKuHe2TGd3xJDtLxIS/ClS6c/rGUrhoM9GXG2ns5qpoyf+vwL+B8++x185eKKrXNZdSkASv9S3ssU2KmpYtel43IpdYlE/O7rOeO6MU5n2750axsP/Zvn8YHf/Bv8f+fu2DqXUsfFWvz0SXX8eeelK2vK3+JoMqL3a0KIlkhez1UMK0NOhMX+lqJs5Cr46Be+ix/9zLdxbT1n/As6mC2c1s7CeBAbuQrKNaWF45vq+J+cjur9mhDCFr5OHale4ojgE9GjRHSFiK4R0ac03ici+rT6/gUiesiJ69phNWMsOAmHBedXn76I83cyCHhd+Lvr9lr6mu121c7JmTFs5qut7EK+vHWir+Z42Ie0gEjwTTLdc4WcHf+vv7GObKmGYqWOP3rxLVvnsmPhP7gQR6HawA3VrXZhMaO8Ph+zdU8AEA95kS6JjH/ZePwF/5aivHhTeeYLlTp+6vMvomYji9pst7F27kookTo3NpR+GJd5I/dZ+4Iv2hNiJVOG120vKsgKtgWfiNwAPgPgMQD3AfgIEd3XcdhjAO5Wv54E8Fm717UDY0zIwnS6gNr5xTQ+dGYW7zqWwHdvbts6lx0L/94Z5cHmbp2Xb6dwLBk2vQGmxYSgSItskjk94V5YTOPe2TF86Mwc3ljOCtec0cKqDx8AHlyIqfeTad3XXRMhy52u2hF1w6xlK8YrrJAPqWLNsd7OL97YRtjnxq//yP1Yy1ZwU21AZAWz3cbaeeiucQDAudvKZ/DKag5Rv8f2ChcQd6lxg9NOVJAVnLDwHwZwjTF2gzFWBfBFAE90HPMEgD9kCi8AiBPRrAPXtsS22mnJKI16Iux3LA5/M1/BZr6KkzNjePhoAtfW85bdReVaA5V607IP8yQX/BXFrfDSzW2882jC0rk6GQ97DQWn1mhiq2BcqGoiokSJOFHeotlkuLiYwYMLMZxeiKHaaO5ya5mBMYZsybqFfzwZQcjnxoXFNADg/J1MaxKwSyLiE9r3WM9VDMd/POxDo8laOQd2eeHGFs4emcBpdSXzxkrW4De6Y3UPC1BcOnOxAF5Uja7LKzncMxN1JOM1FvQiV6kbTpJOhOFawQnBnwfQ7hBdVF8zewwAgIieJKJzRHRuY2PDgdvbC0+jNrJwuD/OCcHh4nJqJoqHj04AAM7dsmbl5yzUYm8nEfFjKurH5dUcXl/OIlep45FjE5bO1YmI33czr9QBN0pjT4R9qDWYqaYS3bi5VUCuUseZhTjOLMQBABeW0pbOVao1UG8yyysst4vwwFwMF5Yy2MpXsJQuOSb4E2FlktQTHGXC7V5HisOzfp3w42/mK7i6nsc7j03geDICj4ssT7iAvT0UIsLDRyfw3ZtK8MTl1SxOzdh35wDKZ5IxGE6SenWkeokTgq81LXY+bSLHKC8y9hRj7Cxj7GwymbR9c1qIplHHgl7UGgwldXPHDtyaOTUTxen5GAJeV8vCMMtOLXzrLphTs2O4vJrFCzcUv+ojxxyy8EM+pAr6VvnOhGvswwfEon6MuKi6T04vxLAwHsR4yIsLdzKWzpWzUJq6kwcXYnh9OYuXb6fUn+OWz9XORNiPRpPpRoq0JlwBCx9wJtuZuzAfOZaAz+PCialIy3duFsaYEpZpccIFgIePJrCRq+A7N7aQLdcdE/xWDSidSVKpI1Xqeww+4IzgLwI41PbzAoBlC8f0DdHKeGZrY+hxZTWHyYgfCbVf60N3jePcrZSlc1nNMmznbYfieH0liy98+yaOToYda8IwHvKh3mTI6RSdWxeIAQd2yis4EZp5fjGNgNeFu6ciICKcXojjwpI1wd+po2N9/E8vxFCpN/GZv74OIuABBzZsgZ0ql3pj1soyNxp/Byfc772Vgs/jarlzTs1EcdmiS6dQbaDJ7E24fJX9619+DQBw35z9kEygrSSLjrs2U6qhXGv2tfEJxwnBfwnA3UR0lIh8AD4M4JmOY54B8FE1WucRABnGmL24RBusCgqOk4J/eTWHe9uiAO6ZjuLWlrVNK6u18Nv5+fcdx3vvSWIlU3bMnQPsWIVpHT/+mkGWZ+tcquA4ESlyYTGDB+ZirTDEB+djeHMth1LV/Oqt1XzGxgrrXccSmB7z4/ydNB5ciDuyYQ4oFj6gb5WLrnBFrFVRltIlLIwH4VXH/+TMGJYzZUufLScm3OPJMCYjPlxdz+Pn3nustZFrF5GSLFx/+tn4hGP7KWOM1YnokwCeA+AG8AXG2GtE9HH1/c8BeBbA4wCuASgC+Ed2r2uHtWwFibAPPo/+fNcSfIFddz0aTYY313L46UcOt16bjysdhTKlWus6onDBidmwcAJeN/7zT78Dn//WTTx+2rn981Yd9WK1Ff7WCc+yTRiUYnZywr26lsMTb9vZNnpgfqz1dzlzKG7qXDvtDa0LztRYAC/+ygeQLdfgM1mPR4+EgBuGT7iiLh0nJtyl9O7SHTwE8spqrmVti2K1UmY7RITf/al3wEXA2SPOGTw8c1xv/J0ohW0VR8wKxtizUES9/bXPtX3PAHzCiWs5gWizaKcE5/ZWAZV6sxUdA+yUaV1MFRELmlvOO2HhA0o28Sfef8LWOToR8buvC2TZAs6Nf6FSR7Zc31X7/dCEMhktp0umBT/nwITLsfs37ESkj8AGn3ANwkDDPjd8bhe2Hci2XU6XcOrkVOvne2cUF8rl1axpwXdiDwWA6euKIFKSZdWgjlQvGclM2zWdXrbtOCU43HVzfGonk5KXadXqr2mEEz78XiEiOGsCORDAjsvE7vjvLKF3rsnrwC9ZSPN3asLtBULjn60gYZBlC/ACal7b9Ywq9QY2cpVdtZqmx/yI+j24vp7X+U1tnHDp9AqfR6kBpbeHspJRylokDRLfesGICn5FaDnllODzqJT2uNv5loVvRXDq8Lld8Bu4pPYDkTrqIlm2gNJ0PeIXy1zUY61Vt2RHcOIhL4Jet2ZDdyOccOn0ioDXjZDPrZswuJItY07QuoyHxEoF6MEt2vZ+C0SEuXjQ0vjnHNhD6SUJg5Isa5kykhF/az+jnwyeYvQY0RhkQCkGRiTesqwbfJOsfUYfD3kR8rmtCb6adNLP1miiiNRR3zBRila0NokeWqWYFcHRbihuRK5ch9dNCHgH8+Oj1CDScymUhP3HTow/L8Xdmck6EwscuAkXUFxlRhPufrhzgAMo+M0mw3OvreK1Ze2Qu42cEoMs8sC7XISoExZmtoLJiG/XjE5EmI8HsZQu6vymNplSzbb/slfs1FHXHrNKvYHNfFU4y3As6LU94XYrVMUbipuFZ9kO4oQLqCUpdAIN9JqXd6IIvr3ENz7GneW35+IWBd9ie8l+oWQ760+4+xGSCRxAwScCfvGLr+JLryxpvr8qGIPPiTmwpF3PljVXFAsadblFsFNHpx9MhL1dN21bG1aC7RRjQfsT7kqmhHjIi0BHO8j5eBDLFl0Kg+pOAPQt/Hyljly5LmzhOzHhcsHvvObMWBCb+Yrpmka5ch1+j2vP33NQSET8uhb+akZa+I5BpDSY4MLeCffninand2JJq2xS7p1gFsZDFl061muB9wO9Esl8ed+teXYnToz/aka7FPZsTCmTW6mbi8W3U8elH4yHfV07hZmNEHHEpZMpYTLi3yPQ/B7WunxWu2GnUmk/mFTLizQ0ylvwiLF+Nz7hHDjBB4CpqL+VzdkJj8pYEOxu44jgd6lMOD8eRKZUQ06gYUI7OYvdrvrFRNjX1cJvNS8XtvAdEPysdho730TsbOhuRK5svVJjP0iElQJqWuUt+PiLutTiQR/ylbqtUsZKDP7e63GLv5tx1g2l+ckAj3/ED8a08xe0Isb6yYEUfD0LfyldQsjnFk52sis49UYTm/mK5iYln3TMhgYOuoU5EfZ1jVLgPtv+WvhlTYtqp6G4WcEZdJeaH5V6E0WNLOKdDWzR8VeE1Y5bZzld0myfyUXP7D7KoFv4CZ2SINy4kD58B5keC2AtW9G0cJZSJczHg8IbbnY3rTbzVTCmvWfABefOtvgDr5TmrQ+04MzGAkgVa62OQu0sp0sYD3kR9In5X+MhH8q1pmm3C0dvk3iuJfhWBGeALUydWHwuOEZZtpxYyF5oMmOsu+Crr5ldYWWHYA8FgKbRs59JV8ABFvxqvYm0RqTCcqbUioEXgW9aWS2R3KpborFnwD8EZpa0lXoT1UZzoJe03HrUisBYTpdM1RAZs5kLwQuFaX3AZixamMqm7eBOuHpVLlcyZSTCPuENT7u5KNlSHcVqQ3P8I34Pon6P6Uid3ICvcCdbBdS6u3T2o6wCcEAFn1tza7m9D9JSStva6EYs6EW10US5Zs2HqVeoKhH2gQitVoMi8KW12fo7/YR/uLm/uJ2VTHlXAo4RdusZ6X3AAl43JiM+LGvcZzdqDcVVMsiCw5PatDZDzcTgA/YFn4cnTnapmzQTC5i38EuDbeEndMordIsY6xcHUvC5+6TzQSpW60gVa6Zamdl94PUqQ3rcLiR0/N1aOFE4qtfwpfqKhm/crIVvd/y1kq7aUWLxxQUnr2Z5DrJLZ15nb2jFZEig3fHnGdfd2jcqyVcW9rAG+PmPh3xwUXcf/n50uuIcUMFXBpQv5zl86d5Xwc/oV4acjPhNWfit5icDbGF2s/C1ipgZYXf8jWrvT0XNjf8wTLiKy8alGfJrttNSLKgItdVNW154jdfW72TWZLZtudZAtd4c6AnX7aKugQuLJj0MTnMgBZ9vSHX6xvkHwIwP37bgZ8tIRrtXhkyaFZzSYNcRARRXyUTYtyepiU8AohE6gBMuhSq8buq655GM+rFhYoWVGwILv5XF3SH4pWoD6WLN0gpLaz9MBJ4AxpvZdDIbC2IjXxEO+7Tb3rNfzMf3JlUyxnB7q4gjifA+3dUBFXy/RxGcTsHvVtNDj7hq4dhx6eiFYCVNWviDXCmzndlYACsdLoUlnnTVxxXWdr6K8ZCva1RWMuLHVr6imSSjRXYIVliAmtTXUbaDu3jMuHR8HheCXrf18Tew8GdiATAmvo81DCssALgrEcbtrd3jv5atoFRr4Oikdp+IfnAgBR/QTr5aShfhdpFQpUaOXcHZzFWQ1Gn0wS1M0SigQS7N247WUn3FguDYLZG8Vai2wuS0SEb9aDLxvq3DIjjz43st/OsbSini48mI1q90xU4uRKpYRcDr6hqGyz8bovtYg15Hh3N4IoSldGnXyuXmplIm/ciktPAdRyv5ajmtbJgY1QFvZ2dJa63rT6pY7bphBSiCU603W12sjMgOgUsBUJbqnYJ/J6VMuGaSTuyWSN4uVFqJMFrwCqbiFuZwjP/CeBCpYg2Ftt7C19Ta88eS5gTHjuBv5atdrXvA/Pjz+4iHBnvCvSsRQqPJdk26vC/G0Lp0iGiCiJ4noqvqv5qNIYnoFhFdJKJXieicnWuKMqMmX7XDk67MYKdEMmMM2wIWJmBCcEq1gS4cxZmNB5Ap1VCs7gjOldU8jk6GDVtLdmJHcJTx119hARD24w+TSwfYHalzfSOPmbGA6SxVuxZ+N/89YF3wBzksGVAsfAC4vb3j1rm1WYDP7RrqTdtPAfg6Y+xuAF9Xf+7G+xljb2OMnbV5TSGmxgLYbNsMYozhxmah1dpOFDslkku1Bir1ZqvtnxZ8SWvGhznoDzuwszHbHvJ4ZS27q82jKHYqNm4Vqrqt/Hh8+Kbg+PNNQ6eajvcKbtgspnYE5/p6HsenzFuXdirGbhequs8/X32ZFfxBn3APq1b8W6pVDygunbsSIcPWnr3EruA/AeAP1O//AMCP2jyfYxwaD4IxtDZO1rIVbOYreGB+zPS5rD7wvCY8b+ythXkLc7ArZXI6QzPzlTrubJdwatq84FstkVytN5Er13VXWFzwRcc/U6oh6vfs64dWhEM8Fl91KTDGcH2jgBMm/feAAxa+zvj7PUpdK+HxLw6HhT8V9cPvce3auL21VdhXdw5gX/CnGWMrAKD+O9XlOAbgr4joZSJ6Uu+ERPQkEZ0jonMbGxuWb+zBhTgA4MJiGgBwcUlpiHJ63lzDcMD6A88rRsYd9mEOckgmZ7Zl4SuC8+ZaDgAsWfiWx1/dd9ETnLDfg7DPbcqlFhtw/zGgTGQ+904s/lq2gnylvquvsii2XGp5fQsfMBeanCnVEPS64fcMtkvT5SLcNRFquXSaTSUkcz8jdAABwSeirxHRJY2vJ0xc592MsYcAPAbgE0T0A90OZIw9xRg7yxg7m0wmTVxiNyemIgj53Dh/Jw1AEXwXAffNWbDwLT7wPPJDT3BiQS+8bjLl0hkGC38uHkDE78GFRWWivbKqCP6pGfPjHw/6LI0/r2WiN/6AOcFJl2oDv2EIKIIz39Zgh0foWLXwi9WG6RLJ1XoTuUpd16UGmAtNzpSGw6UJAIcTIbylWvgr2TIq9ea+RugAgKGpyBj7QLf3iGiNiGYZYytENAtgvcs5ltV/14noaQAPA/imxXsWwu0iPDAfw3lVcC4tZXA8GUHIZ946jgW9put9AG1p5ToWDhGZeuCzpdq+LwtF8Lhd+L4j4/jOjS0AiuCHfG7hPgTtWHWpiUy4gEnBL1ZbuRmDzl0TIVxdVyZaHqFj1cIHFLHtVhNHi7RBWQVOMurHeXUlbnjOIZlwAcWP/+1rW8r+oTrhHh1yl84zAD6mfv8xAF/uPICIwkQU5d8D+CCASzavK8SZhRheX8miWm/i4lLGkjsHsF4iOWVGcEz4kAe5UmY77zqewI2NAtayZVxezeKe6ShcFnzfsaAX5VpTs9yyHrzrlqGFaWL800Pi0gGAH7gniTfX8ri5WcC19Twifo+pHBSO1VyUbQGXGmDepTMMK1xAsfBLtQaWM2U8//oa/B4XTi9Y0yCnsCv4/x7ADxHRVQA/pP4MIpojomfVY6YB/C0RnQfwXQBfYYz9pc3rCnHmUBzVehPfurqBjVwFD1gUfKslkreLNRAZbzCJPvDNJkOmVDP0iQ4K3398EgDwraubuLKawykL/ntgJyLDbKTOtirihoJjxqVQrCE+JILz6AMzAIAvvbKIr1xcwcNHJyw1XrdaE5+3WRTx4RerjV05A93IDpFL5z0nJkEE/NELt/GVCyv4wH3T+964xZapyBjbAvCDGq8vA3hc/f4GgDN2rmOVM+rG7X967goA4EGLs2t7iWTRxh2AYuHHgl7DiI5k1I9X72QMz5cr19Fk+pvAg8S9s2MYC3jwvz3zGnKVOt5/qtuevj7tFqZW57BubBeqIDIer8mIH5lSDZV6Q3czkDE2VD7k+XgQbzsUx+/+9XU0mgyfeP9xS+exWqK6tcLSicMHdocmhw3CXTOlGh4YkvE/lozgh+6dxn/+m+toMuCJM3P7fUsHN9MWULINZ2MBXFvP48fePo8zh+KWzmNnSauXZchJRvzYLhjXc+F7AsNiYbpdhHceSyBXqeNn33MUP3z/jKXzWB3/LTUGXGTCBZTuZHoUqg3Um2xofMgA8PjpGTSaDO8+kcA7Dk9YOofV8ecuTRELHxArr5AeohUWAPzce4+jyZQSIe89aT0IxSmGwxlsESLC0z//bnjcZGqzqZP2B95Madm0QVkFDq/nslWodC3jCyj+YwAY14nrHzT+8XuO4uhkGP/LD5+0fA7LE65BljOnPTRWLxM73Zpwh2OFBQA/cmYef/byIv75B/s//lutsGRjlyZgHJpcrTdRqjWGZoUFAO84PI4PnZnD8WR4IEJJD7TgA860ErMuODXMC9R+b3/g9QSfW/ixIRKcdx5L4J3HErbOYUdwzAi+UbYtLxE8LJu2gPL8/9U/e6+tc9ix8McCHngNaleJJh+2yioM0fgDwG9/5O37fQstDrRLxynsPPAiG6yiFg73oY4P2QNvFzsWvlGEDmBecIbJpeAEXrcLIZ/5EsnbxVrXxj/tjKsdogyf/yGpozPISMEXwIrgMMYUH76I4EQUq97ogW/58Idk09YprJZIFnXpJMJiEy638Edt/AFlkrNm8BiLs9tFSAhESmVKfIUrBd8qUvAFsCL4JbUVm4gPfzKqFpAysDDTgmGeBw0rJZKbTYa04ITr87gwHvIaC/4IC85Y0Gu665WoSw0QC42VFr59pOALwGNnzQjOditCwfjhDPk8iPg92MzpR4mki1WMBYzDPA8iZstbZMs1UyGsIrkQw1KLvRfELFQsFXVpAmLJb1Lw7SMFXwC3ixANeEw98KkC97c798APU1q505gtkZwyud8xGREQnOJw9CLoBWYn3JZL0yAGnyM04Y6wS80ppOALYvaBF00r5yhLWv16PalibWQfdrM+ZJE6Ru2ICE66OLoTrtnnv1BVXJoieSiAMv6b+QqaOrkoPCx5GKrFDipS8AUx+8CLFo7iiFk41ZGLEOGYHf9W0o9JH7Je+Yx0aXgKpzlNP8a/1mC618iUaoj4PaZalEp2I0dOENMWvmCWIUdE8FPF2siFZHJiJjcNzbp0klE/SrUGCtXuBdrSxeEpq+A08ZBX7eAmVsCuVanUxPMP6AcuDFNZi0FFCr4gViwcMxE1yagf2XJdtyJkqlgdWZeO2RLJaZMhrCK5EJkhqpTpNGYj1VouTRM+fEB//LNDVClzUJGCL4gVH35coHAahxeQ6lZPpN5Q2vWNsg+5UhcvkZwqVuF2kbC/V6SeS6Y0XHVcnMRsxVJeKdOsha83/pt5sUQ6SXek4Ati1sJMFWvC/kvA2MLh1x6W0shOY1ZwUmqRLdFywCIW5qhv2gLiFn7Kwh4WoD/+m/kKJgVXDBJtpOALEgt6UTVjYZqIQQbamml3eeDTIxwDDpgXnHSxamqskgbjX641UKo1RtelZtalU6jCY2KFFfV74PO4uo4/Y0wVfOtFECVS8IXhD7zoxuG2ScHnFs56N8Ef0bIKHCuCIxoSC6BVRrmb4OyUtZATrggptVKs6ArLqNVnsdpAudbEpIWOXZIdbAk+Ef0kEb1GRE0iOqtz3KNEdIWIrhHRp+xcc7+w8sBPmChjPBlRCkitZ7Vj8Vt1XEbUh2zewjeXs+ByERJhX1fB4a/rVTM9yPCxFG2CspUX6wXRjl7yIfftSwvfHnYt/EsAfhw6DcmJyA3gMwAeA3AfgI8Q0X02r9t3zAgOY8y0D9/jdmEqGsBKl2bpO2GG0sIXIVUUK9zVjp7grGcrrWNGEe6aSZuy8C2Mf5cJlwu+UfcsiT62BJ8x9gZj7IrBYQ8DuMYYu8EYqwL4IoAn7Fx3PzAjOEU1y9CsOE/HAljtYuHzRJb4EDU/cRJLE67J8Z+K+rHWZfz5RGClCfhBwGwBO6U0tbmx0hP8DbXOVFJa+Lbohw9/HsCdtp8X1dc0IaIniegcEZ3b2Njo+c2JYkZwzCadcGbHAljtYuGvZssI+dyIGvT8PKiYKZHMK5Wa3e+YiQW7jj+38EfZpWAmNHm7YMHCj/ixXayi1mjueU+6dJzBUPCJ6GtEdEnjS9RK19q16Zq/zhh7ijF2ljF2Npnc/x6QHDOCbzYkjTMT6y74a9kypscCwptgBw0zFqbZLFvOXCyArUJVMxJrI1/GeMgLn2d04xxEC9g1mgzpUs2SD58xxf/fiXTpOIOhucgY+4DNaywCONT28wKAZZvn7DtmSiRzwTGzaQsAs7EAcpU6cuVa63qctWx5ZN0JHFEL02wdF86s2s92JVPG0cnwrvfWs5WR9d9zYkGxCTdTqoExCwbPmLIhvpIp7WlNupVXwmyN2iVK9OnH6L0E4G4iOkpEPgAfBvBMH67rKGZKJLf87aZdCspDruVHXstWHOnPO8yI1mQ3WymTM6eO70q6tOe9jbx+v+FRIB70mXNpmhT8+XFlwl3SGP/NfEVm2TqA3bDMHyOiRQDvAvAVInpOfX2OiJ4FAMZYHcAnATwH4A0Af8oYe83ebe8PohamVR8+t3BWM7s3rhhjLZfOKCNaQM2yS0e18Jc13GrSwhcff9uCn9IWfOm/t4+tHUDG2NMAntZ4fRnA420/PwvgWTvXGgSEXQrFKlwE04WeZmPcpbD7gc+UaqjUm1Lwg15c38gbHmc1SW2mi4XPGFMt/NEWHNHyImYrxXLGAl5EA54uFn4V982NmTqfZC/SIWYCM4IfVzM3zTA1pghK58btmhohMj024oIj7MO3VoYi4HUjEfbtsfCzpTqq9aa08AUL2KVMNv9pZz4e1LbwcxUZkukAUvBNYEZwrKTgB7xuTIR9WOnw4fPY/JG38IUtzArGAh5LG3yz8cCeFdZGXhn/URd80QJ2Vl06ALAwHtxj4ZdrDeQqdVk4zQGk4JvAjA/frP+eMzMWwNoeC7/cem+UEbUwtwpVJCxag7OxIJY7BGfUs2w5oqHJ24UqQj63pd6/Whb+ljqBWP2bSnaQgm8Csy4dK8zG9pZX4BPAqAuOqIVppawCZy4WwEp69/jvZNmO9oQbFxR8s5Vi25kfDyJXqe+6xmZOJl05hRR8E4wJlkjeKlhv1DAT2+tSWMspST9WLKaDhKiFuZWvYsJkWj9nNh5s5UJwpIWvIFoxdrtorlJpO/PxEIDdkTpvbRcBAHPx0Z5wnUAKvglEBIcxhlShajkj8EgijFSx1orlB5RN21H33wPigm+2Umk7c23JV5yNfAV+j0u4tvtBxYxLx7Lga8TiX9/Igwg4noxYOqdkByn4JhB54LOlOupNZvmBv2cmCgC4spZrvbaWLWNKCr7whKsIjjVrnCdftVuYt7cKmI8HR7asBacvgh/nsfjF1mvX1vNYGA+O/ArXCaTgm0Dkgd8q2Kv5cYoL/upuwZ8Z8ZBMQGz8c5U6ag1m2aV2YkqxIi+3jf8bKzmcmo1aOt9BYqwPPvzJiA9+j6vDwi9I694hpOCboCU4Oj7MrVZImjWBnor6EQt6W4KTKdawlq3gcCJs8JsHHxHBt1pHhxMP+XBoIoiLS2kAQK5cw1vbRdw3K5N+3C5C1KCAXbnWQKHasOxSIyIsjAdxa0ux8BtNhhsbeZyQgu8IUvBNIGThq5X+rFqYRISTM1G8qbp0vncnBQB4+6G4pfMdJERKJLdC+GzUXTk9H8PFpQyAnZXWvVLwASi5EHpRUjtJV9ZXpGcOxfHK7RQYY1hOl1CpN3F8Sgq+E0jBN4GI4G+3YoatC86pmSjeXM2BMYbvvZWGi4AHpeDD43Yh6vfoRomkbCT9cE7Px3Fnu4R0sYo3VrIApOBzYkGvbternaQr6416vu/IBLYKVdzcLODaulJK44QUfEcY7bADk4wFvSDaqdWixbbqw7cjOPdMR5Gr1LGULuGVt1K4ZzqKyIg2PunEqCb7liOCHwMAXFrK4vWVHGJBL2ZHvFIpxygXhZe1sNOK8/uOTAAAXrq1jVy5DgDSpeMQ0sI3gdtFmAj5WqKixVahiojfA7/HekQB37i9vJLDq3fSeOjwuOVzHTSMBMdOWj/ngXnFmr+4lMEbK1ncOxsd+QgdjuH426ijwzmeDGMi7MNLt1K4tp7HRNhneU9GshtpNpokEfFpduThbOWtx+Bz7pmJwkXAb3/jGnLlOh66Swo+x9jCrMLncSHksz7h8o3bv3lzHVdWc/jww4eMf2lEMBT8vP0VLhHh7OFxfOvqBgiEu6U7xzGkhW+SRNjfaremhZ0YZM5YwItfefxenL+TBgA8dFfc1vkOEkY+ZJ7lbNci/77DE3jhxjZKtQbeLifcFsYWfg1EO/tdVnn46ATWshWU6w186rFTts4l2UFa+CZJRHx4bTnb9f2tQhXzDqSA/+O/dwzTYwF89+b2nnZ7o0wi4sN3b3VfYaUcmHAB4H//8dP4mfcchdtFODktY/A5sdBOeRGtRKhUoYpY0AuPzVaEHzozh9eWs/jE+0/IDVsHkYJvksmIkYVfwel5ZyI6PnRmDh86M+fIuQ4KkxE/tgtV1BpNzfLHWw4JfsDrxgPq5q1kh/Z6OjOxvYK/VahYrhTbzvRYAL/1P73N9nkku7Hb4vAnieg1ImoS0Vmd424R0UUiepWIztm55n6TCPuQK9dRqe8toGY3rV9iDC9gtt1l49wJl5qkO0ahyevZSquRj2TwsOvDvwTgxwF8U+DY9zPG3sYY6zoxDAO8JreW4GTLSlq/bNTQO3iJ3I2c9irLKZeORBs+tltdVrnrOdnsfZCxJfiMsTcYY1ecuplhgEfgaEXqOBESKNGHW/gbGoLDOyPZybKV6MPFfF1jwmWMYT1XHvnev4NMv6J0GIC/IqKXiehJvQOJ6EkiOkdE5zY2Nvp0e+Jw613Lj+9E0pVEn6SOhc/r1svKor2D91Vez5X3vJct11GuNWUp7wHGcNOWiL4GYEbjrV9ljH1Z8DrvZowtE9EUgOeJ6DJjTNMNxBh7CsBTAHD27FkmeP6+kVD981oW/k4dHWnh9IrJaPcJd1W2guw5Eb8HQa+7Nbm2s6FOAtKHP7gYCj5j7AN2L8IYW1b/XSeipwE8DDG//8AxqS5XeRnkdribgYuSxHlCPg/CPremhd8SfFkGoWcQEabG/FjTGP+1rGwFOej03KVDRGEiivLvAXwQymbvUBL2ueH3uDQt/JV0GW4XyQe+xySjfmxqjD/v/StdCr1lKurHenavS2ddWvgDj92wzB8jokUA7wLwFSJ6Tn19joieVQ+bBvC3RHQewHcBfIUx9pd2rrufEBEmI37NTcPlTAnTUT/cLll3pZdMRvwt90E7q9kygl73yLci7DVT0YD+HorctB1YbH0yGGNPA3ha4/VlAI+r398AcMbOdQaNbvV0VtJlzKot2iS9Ixn146paNred1WwZM7GALHTWY5JRP/7mTW2XTsjnlpVdBxhZS8cCibBP04e/nCnJMrp9QLHwNQQnU25FkUh6x/RYAPlKHcVqfdfrPCRTTriDixR8CyQi/j0WPmMMK5lyqwmzpHcko35kSrU92c6r2bKM0OkD3GXTGamznqvIkNgBRwq+BbhLh7GdqNGtQhXVelNa+H2AZ9u2T7qMMaxnK5iW499zplqx+B2Cn5VJV4OOFHwLzI4FUG00d23crqSVTUTpw+89Wslv24Uqqo2mtPD7AI9CW2uL1FGybCsyQmrAkYJvgcNqueLbW8XWa8uZEgBgLiYFv9e0yiu0WZgy6ap/tFw6beOv+PQb0sIfcKTgW+BoQhH8m5uF1msraUXwZx2ohS/Rh1uRy5kdC5Nbm9Kl03viIS98bteu8gpc/GUM/mAjBd8CC+NBeFyEW+2CnynD53HJwl19YDYWQNjnxvW20MzVjCI40sLvPUSEZNSPjbZN25sbymfhrgnZrGeQkYJvAY/bhUMTIdza2hH85UwZszIGvC8QEU5MRXB1Pdd6bTVbBtGOu0fSW2ZiASypq1oAuLKm/C3umZbdqQYZKfgWOZwI4dbmjg9/JS1j8PvJiakorq7tWPhvruZweCKk2QVL4jynZqJ4YyXbilS7sprDfDyIaMBeL1tJb5GfDoscSYRxa6vQeuAXUyW5YdtH7p6OYD1XQaaodF66uJTB6YX4/t7UCHH/XAzZch2LKcXKf3Mth5MzsvfvoCMF3yJHJ8MoVhvYyFWwlC5hNVuWPVD7yN1qY+trGzls5pW/wYNy/PvGA2rf5ktLGdQaTVzfyEvBHwJk0QuLHJncidThvsxHjiX285ZGinumFXG5upZHtqyk+J9ekILfL+6ZjsLtIry2nMXxqQhqDYaT01LwBx0p+BY5kggBUGLxX76dQizoxSlp4fSN+XgQAa8LV9fzWM9VQATcPze237c1MgS8btw9FcGl5UzLsr9HCv7AIwXfIvPxIHxuF164uYVXbqfwfUcm4JJlkfuGy8UjdfLwuV04NhmWG4Z95v65GL55dQOn53NwuwjHp2RI5qAjffgW8bhd+Nj3H8aXXlnCra0iHjk2sd+3NHKcmhnDize28HfXN/Gg3LDtO/fPjWEjV8GffPcOjk2G4fe49/uWJAZIwbfBL33wJE6om4fvPCr99/3mlz54D95/cgrFakNOuPvAO49NgEhJRPx3P3Z6v29HIgC1V3wcNM6ePcvOnTu337ehy5XVHL70vUX8yx8+JV06+8R6rozJsF+O/z6Qr9Rlw5MBg4heZoyd1XrPbovD/0REl4noAhE9TUTxLsc9SkRXiOgaEX3KzjUHjZMzUfzyY/dKsdlHpqIBOf77hBT74cKuS+d5AA8wxh4E8CaAX+48gIjcAD4D4DEA9wH4CBHdZ/O6EolEIjGJLcFnjP0VY4z3OXsBwILGYQ8DuMYYu8EYqwL4IoAn7FxXIpFIJOZxctP2ZwB8VeP1eQB32n5eVF/ThIieJKJzRHRuY2PDwduTSCSS0cbQAUdEXwMwo/HWrzLGvqwe86sA6gD+WOsUGq913SlmjD0F4ClA2bQ1uj+JRCKRiGEo+IyxD+i9T0QfA/APAPwg0w75WQRwqO3nBQDLZm5SIpFIJPaxG6XzKIB/CeBHGGPFLoe9BOBuIjpKRD4AHwbwjJ3rSiQSicQ8dn34vwMgCuB5InqViD4HAEQ0R0TPAoC6qftJAM8BeAPAnzLGXrN5XYlEIpGYxFYQLWPsRJfXlwE83vbzswCetXMtiUQikdhjoDNtiWgDwG2Lvz4JYNPB23EKeV/mGdR7k/dlDnlf5rFyb4cZY0mtNwZa8O1AROe6pRfvJ/K+zDOo9ybvyxzyvszj9L3J4mkSiUQyIkjBl0gkkhHhIAv+U/t9A12Q92WeQb03eV/mkPdlHkfv7cD68CUSiUSym4Ns4UskEomkDSn4EolEMiIMteAT0U8S0WtE1CSisx3v/bLacOUKEf1wl9+fIKLnieiq+u94D+7x/1WzkF8loltE9GqX424R0UX1uJ63+SKif01ES2339niX4/ravMZEU52+jJfR/58UPq2+f4GIHurVvXRc9xARfYOI3lA/A7+gccz7iCjT9jf+tT7dm+7fZj/GjIhOto3Dq0SUJaJf7Dimb+NFRF8gonUiutT2mpAe2fpMMsaG9gvAvQBOAvhrAGfbXr8PwHkAfgBHAVwH4Nb4/f8I4FPq958C8B96fL//B4Bf6/LeLQCTfRy7fw3gnxsc41bH7hgAnzqm9/X4vj4IwKN+/x+6/U36MV4i/38oGeVfhVIV9hEAL/bp7zcL4CH1+yiUBkSd9/Y+AH/Rr2dK9G+zX2PW8XddhZKgtC/jBeAHADwE4FLba4Z6ZPczOdQWPmPsDcbYFY23ngDwRcZYhTF2E8A1KI1YtI77A/X7PwDwoz25UShWDYD/EcCf9OoaPaDvzWuYWFOdfiHy/38CwB8yhRcAxIlottc3xhhbYYy9on6fg1KnqmufiQFjX8asjR8EcJ0xZjWL3zaMsW8C2O54WUSPbH0mh1rwdRBtujLNGFsBlA8QgKke3tPfA7DGGLva5X0G4K+I6GUierKH99HOJ9Ul9Re6LB9NNa/pAd2a6gD9GS+R//9+jxGI6AiAtwN4UePtdxHReSL6KhHd36dbMvrb7PeYfRjdDa/9GC+OiB7ZGruB70BMAg1YtH5N47WexZ8K3uNHoG/dv5sxtkxEU1Cqj15WrYCe3BeAzwL4N1DG5d9AcTf9TOcpNH7X9jiKjBfpN9UBejBeWreq8Vrn/7+vz1onRBQB8N8A/CJjLNvx9itQ3BZ5dY/mzwHc3YfbMvrb7NuYkVKi/Ueg0X8b+zdeZrA1dgMv+MygAUsXRJuurBHRLGNsRV1SrvfiHonIA+DHAbxD5xzL6r/rRPQ0lKWbLQETHTsi+i8A/kLjrZ40rxEYL6OmOj0ZLw1E/v/71uCHiLxQxP6PGWNf6ny/fQJgjD1LRL9LRJOMsZ4WChP42+xnU6THALzCGFvrfGO/xqsNET2yNXYH1aXzDIAPE5GfiI5CmaW/2+W4j6nffwxAtxWDXT4A4DJjbFHrTSIKE1GUfw9l4/KS1rFO0eEz/bEu1+t78xoSaKrTx/ES+f8/A+CjauTJIwAyfFneS9Q9od8D8AZj7De7HDOjHgciehjK532rx/cl8rfZlzFT6brS3o/x6kBEj+x9JvuxI92rLyhCtQigAmANwHNt7/0qlN3sKwAea3v981AjegAkAHwdwFX134ke3efvA/h4x2tzAJ5Vvz8GZbf9PIDXoLg2ej12/xXARQAX1AdmtvO+1J8fhxIBcr1P93UNio/yVfXrc/s5Xlr/fwAf539PKEvsz6jvX0RbtFiPx+k9UJbyF9rG6vGOe/ukOj7noWyAf38f7kvzbzMgYxaCIuCxttf2ZbygTDorAGqqhv1sNz1y8jMpSytIJBLJiHBQXToSiUQi6UAKvkQikYwIUvAlEolkRJCCL5FIJCOCFHyJRCIZEaTgSyQSyYggBV8ikUhGhP8ftOmyoyc9ggUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "54111805",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calXnew(Xold):\n",
    "    return ( 1 / Xold)+( Xold / 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "100e400a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finshed, the result is 1.4142135623730954\n"
     ]
    }
   ],
   "source": [
    "Xold = 10\n",
    "error = 1e-4\n",
    "i = 0\n",
    "maxIter = 50\n",
    "data = np.zeros((maxIter,4))\n",
    "\n",
    "while(i < maxIter):\n",
    "    XNew = calXnew(Xold)\n",
    "    difference = abs(XNew - Xold)\n",
    "    data[i,:] = [i,XNew,Xold,difference]\n",
    "    \n",
    "#    print(\"i:\",i,\"XNew\",XNew,\"Xold\",Xold,\"Diff\",difference)\n",
    "    if difference < error:\n",
    "        print(\"Finshed, the result is\", XNew)\n",
    "        break\n",
    "    Xold = XNew\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e61320b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00000000e+00, 5.10000000e+00, 1.00000000e+01, 4.90000000e+00],\n",
       "       [1.00000000e+00, 2.74607843e+00, 5.10000000e+00, 2.35392157e+00],\n",
       "       [2.00000000e+00, 1.73719487e+00, 2.74607843e+00, 1.00888356e+00],\n",
       "       [3.00000000e+00, 1.44423809e+00, 1.73719487e+00, 2.92956780e-01],\n",
       "       [4.00000000e+00, 1.41452566e+00, 1.44423809e+00, 2.97124397e-02],\n",
       "       [5.00000000e+00, 1.41421360e+00, 1.41452566e+00, 3.12058346e-04]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:i,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "21d87045",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x20ceb0a3040>]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data[:i,0],data[:i,1])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
